Month: May 2019

Easy Create Mosaic Plot using Stacked Bar Chart

Creating Mosaic Plot

In one of my work project, I need to use mosaic plot to visualize the proportion of different variables/elements exists in each group.  It is hard to find a readily available mosaic plot function (from Seaborn etc) which can be easily customized. By reading some of the blogs, mosaic plot can be created using stacked bar chart concept by performing some transformation on the raw data and overlaying individual bar charts. With this knowledge and using python Pandas and Matplotlib, I am able to create a mosaic plot that is good enough for my need.

Sample Data Sets

A sample data set is as shown below. We need to plot the proportion of b, g, r (all the columns) for each index (0 to 4). Based on the format of the data set, we make a transformation of the columns to be able to have Mosaic Plot.

Breaking down the data transformation for stacked bar chart plotting

We perform two transformations as followed. Mosaic plot requires the sum of  proportion of categories for each group to be 1.0 or 100%. Stacked bar chart can achieve this by summing or stacking values for each element in the group but we would need to ensure the values are normalized and the sum of all elements in a group equal to 1 (i.e r+ g+b =1 for each index).

To simulate the effect of stacked bar chart , the trick is to use multiple bar charts to overlay on top of each other to simulate the effect of stacked bar chart. To be able to create the stacked effect, the ratio/proportion of the stacked element need to be the sum of proportion value of “bottom” elements + the proportion value of the element itself. This can be easily achieved by doing a cumulative sum along the row axis.

As example below, r will be used as a base (since values are based on b + g + r). g will overlay on top of r since it is summation of b + g. b will be final layer overlay on g and r.

Mosaic plot function

Once the transformations are done, it is easy to plot the mosaic plot by plotting the different bar charts and overlaying on top of each other. Additional module adjustText can be used to prevent overlapping of the text labels in the plot. Based on the above, we can create a general mosaic function as below.

 

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Predict Product Attributes from Title of Product Listings

Extracting Attributes from Product Title and Image

This is a National (Singapore) Data Science Challenge organised by Shopee hosted on Kaggle. In the advanced category, the tasks is to extract a list of attributes from each product listing given product title and the accompanied image (a text and a image input). Training sets and full instructions are available in the Kaggle link. This is a short attempt of the problem which include the basic data exploration, data cleaning, feature extraction and classification.

Basic Data Exploration

While the project requirement have 3 main product categories, Beauty, Mobile, & Fashion, I will just focus on the Mobile data set. The two other categories will follow the same approach. For the mobile data set, the requirement is to extract the following attributes such as Brand, Phone Model, Camera, Phone Screen Size, Color Family.  A brief exploration of the training data set observed.

  1. Only title (text) & image (pic) available to predict the several attributes
    of the product.
  2. The attributes are already label-encoded.
  3. There are a lot of missing values particularly like Network Connections etc have more than 80% of data missing. This is quite expected as sellers unlikely to put some of these more obscured attributes in the title description while attributes like Brand and Model should have less missing data.

From seller’s perspective, seller will try to include as much information as possible in a
concise manner especially attributes like brands, models etc to make their posting relevant to search and stand out to the buyers. Using only image to extract attributes such as Brand and model might be difficult especially for mobile category where it is difficult to differentiate from pic even with human eye.

From the exploration, I planned the following steps.

  1. Using title (text) as main classification input and ignore images.
  2. Train and predict each attribute at a time.

Basic Data and Text Cleaning

There are some attributes Network Connections, Warranty Period which have large proportion of missing data. However, those attributes have majority of the observations having a certain attribute. In this case, those missing values are assigned with the mode of the training population (e.g. it is likely for Network Connections , most phones should be 4G etc). The attributes are also converted to integer for training purpose.

For the title, before extracting the numeric features, we perform cleaning on the data set. Since most users would highlight the most important feature in the product tile to make their product stand out and relevant, they would generally have omitted most of the stop words, most punctuation. and white spaces Hence for this data set, I will try minimal cleaning: change the title to lowercase and remove special characters. This can reduce a significant amount of time in text cleaning especially for large data set.

Data Cleaning and pipelines

For the advanced data extraction, I chose the Bag-Of-Word (BOW) model to generate the features from the text columns. In the BOW model, I use TF-IDF approach which computes the weighted frequency of each word in each title. For classification, SVM is chosen as the classifier. Pipe-lining makes it easy to streamline the whole text processing and attributes classification making it run on all the different attributes.

Below is the complete code running from extraction, cleaning to classification.

Further Improvement

This is the starting point of the project and take only a few lines of code to get it up and running for quick analysis.  I will improve the existing code by incorporate gridsearch for hyperparameters and expanding on the pipelines and features in the subsequent posts.

 

Using k-means clustering to detect abnormal profile or sudden trough

Background

For a particular test we are handling, we need to ensure a particular metric A maintain a certain parabolic or relatively flat profile across a range of metric B. In recent days, we encountered an issue where certain samples of the population are experiencing a significant and sudden drop in metric A within a sub range of metric B.

We need to comb through the population to detect those that has the abnormal profile as shown in chart below for further failure analysis. While it is easy to identify by eye which sample are seeing abnormal performance after plotting metric B against metric A, it is impossible to scan through all the plots to identify the problem sample.

normal_vs_abnormal_profile

I decide to use machine learning to comb through the population to get the defective samples. Given the limited training samples on hand and the hassle of getting more data, I will use unsupervised learning for quick detection in this case.

** Note the examples below are set to be to randomly generated as model to the real data set.

Pre-processing

There are certain pre-processing done on actual data but not on the sample data. Some of the usual pre-processing tasks performed are illustrated below.

  1. check and remove missing data (can use pd.isnan().sum()
  2. drop non required columns (pd.drop())

Features Engineering

To detect the abnormal profile, I need to build the features that might be able to differentiate normal vs abnormal profile. Below are some of the features I can think of which is derived by aggregating Metric A measured across all Metric B for each sample:

  1. Standard deviation of Metric A
    • Abnormal profile will have larger stddev due to the sharp drop.
  2. Range of Metric A
    • larger range of max – min for the abnormal profile.
  3. Standard deviation of Running delta of Metric A
    • Running delta is defined as the delta of Metric A for particular Metric B against Metric A of previous Metric B. A sudden dip in Metric A will be reflected in the sudden large delta.
    • Standard deviation of the running delta will catch the variation in the rise and dip.
  4. Max of Running delta of Metric A
    • This will display the largest delta within a particular sample.

Scaling and K-means Clustering

A basic scaling is done to normalize the features before applying the KMeans. All the functions will be from SkLearn. KMeans cluster is set to 2 (normal vs abnormal profile)

Results

This is a short and quick way to get some of the samples out for failure analysis but will still need further fine tuning if turn on for production modes.

Sample Script